This work presents a new approach and an algorithm for binary image representation, which is applied for the fast and efficient computation of moments on binary images. This binary image representation scheme is called image block representation, since it represents the image as a set of nonoverlapping rectangular areas. The main purpose of the image block representation process is to provide an efficient binary image representation rather than the compression of the image. The block represented binary image is well suited for fast implementation of various processing and analysis algorithms in a digital computing machine. The two-dimensional (2-D) statistical moments of the image may be used for image processing and analysis applications. A number of powerful shape analysis methods based on statistical moments have been presented, but they suffer from the drawback of high computational cost. The real-time computation of moments in block represented images is achieved by exploiting the rectangular structure of the blocks.

This paper describes a new method for character recognition of typewritten text. The proposed approach is based on the approximation of character signatures by rational functions. Specifically, after the preprocessing operation, a separation procedure is applied to each character and its one-dimensional signatures are derived. These signatures are then approximated by rational functions via a linear programming technique and according to the minimax criterion. The values of the approximation errors for each signature are specified as character features. Through this technique only six powerful features are derived for each character. The classification technique employed is simple, adapted to the features selected and is based on features’ similarities in combination with the minimum Euclidean distance classifier.